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use torch.testing.assertclose instead to get more details about error in cis (#35659)

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* use torch.testing.assertclose instead to get more details about error in cis

* fix

* style

* test_all

* revert for I bert

* fixes and updates

* more image processing fixes

* more image processors

* fix mamba and co

* style

* less strick

* ok I won't be strict

* skip and be done

* up
This commit is contained in:
Arthur
2025-01-24 16:55:28 +01:00
committed by GitHub
parent 72d1a4cd53
commit b912f5ee43
255 changed files with 1048 additions and 969 deletions
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8
tests/models/dpt/test_modeling_dpt.py
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@@ -342,7 +342,7 @@ class DPTModelIntegrationTest(unittest.TestCase):
[[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(outputs.predicted_depth[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
def test_inference_semantic_segmentation(self):
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large-ade")
@@ -363,7 +363,7 @@ class DPTModelIntegrationTest(unittest.TestCase):
[[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(outputs.logits[0, 0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
def test_post_processing_semantic_segmentation(self):
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large-ade")
@@ -410,7 +410,7 @@ class DPTModelIntegrationTest(unittest.TestCase):
predicted_depth.unsqueeze(0).unsqueeze(1), size=(500, 500), mode="bicubic", align_corners=False
).squeeze()
self.assertTrue(output_enlarged.shape == expected_shape)
self.assertTrue(torch.allclose(predicted_depth_l, output_enlarged, rtol=1e-3))
torch.testing.assert_close(predicted_depth_l, output_enlarged, rtol=1e-3)
def test_export(self):
for strict in [True, False]:
@@ -431,4 +431,4 @@ class DPTModelIntegrationTest(unittest.TestCase):
eager_outputs = model(**inputs)
exported_outputs = exported_program.module().forward(inputs["pixel_values"])
self.assertEqual(eager_outputs.logits.shape, exported_outputs.logits.shape)
self.assertTrue(torch.allclose(eager_outputs.logits, exported_outputs.logits, atol=1e-4))
torch.testing.assert_close(eager_outputs.logits, exported_outputs.logits, rtol=1e-4, atol=1e-4)

6
tests/models/dpt/test_modeling_dpt_auto_backbone.py
View File

@@ -277,7 +277,7 @@ class DPTModelIntegrationTest(unittest.TestCase):
[[6.0336, 7.1502, 7.4130], [6.8977, 7.2383, 7.2268], [7.9180, 8.0525, 8.0134]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(outputs.predicted_depth[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
def test_inference_depth_estimation_beit(self):
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-beit-base-384")
@@ -299,7 +299,7 @@ class DPTModelIntegrationTest(unittest.TestCase):
[[2669.7061, 2663.7144, 2674.9399], [2633.9326, 2650.9092, 2665.4270], [2621.8271, 2632.0129, 2637.2290]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(outputs.predicted_depth[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
def test_inference_depth_estimation_swinv2(self):
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-swinv2-tiny-256")
@@ -321,4 +321,4 @@ class DPTModelIntegrationTest(unittest.TestCase):
[[1032.7719, 1025.1886, 1030.2661], [1023.7619, 1021.0075, 1024.9121], [1022.5667, 1018.8522, 1021.4145]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(outputs.predicted_depth[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)

2
tests/models/dpt/test_modeling_dpt_hybrid.py
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@@ -335,4 +335,4 @@ class DPTModelIntegrationTest(unittest.TestCase):
[[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100, expected_slice, atol=1e-4))
torch.testing.assert_close(outputs.predicted_depth[:3, :3, :3] / 100, expected_slice, rtol=1e-4, atol=1e-4)